# MLOps Pipelines: From Experiment to Production Models

URL: https://www.devopsness.com/blog/mlops-pipelines-from-experiment-to-production-models
Author: Kiril Urbonas
Published: 2025-10-12
Updated: 2025-10-12
Categories: AI
Tags: Kubernetes, Python, LLM

Summary: Build MLOps pipelines for training, evaluation, and deployment. Reproducibility and monitoring.

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# MLOps Pipelines: From Experiment to Production Models

**MLOps** bridges experimentation and production. Here’s how to run reproducible training and deployment pipelines.

## Pipeline Stages

1. **Data** – Ingest, validate, version (e.g. DVC, Delta Lake).
2. **Train** – Reproducible training (code + data + config); track experiments (MLflow, Weights & Biases).
3. **Evaluate** – Holdout metrics, A/B tests, fairness checks.
4. **Deploy** – Model registry, versioned serving (API or batch); canary or shadow mode.
5. **Monitor** – Data drift, performance metrics, and alerts.

## Reproducibility

- **Pin** code (git SHA), data (hash or version), and environment (container image).
- **Config as code** (YAML/JSON) for hyperparameters and feature flags.

## Best Practices

- **Version models** in a registry; promote only after evaluation passes.
- **Automate** retraining or retraining triggers when drift or performance degrades.
- **Secure** training data and model artifacts; audit access.

Start with a simple pipeline (train → eval → deploy) and add monitoring and automation as usage grows.